Deck 5: Text and Web Mining

Question 1

Two advantages associated with the implementation of NLP are word sense disambiguation and syntactic ambiguity.

Answer 1

False

Question 2

A vast majority of business data is captured and stored in text documents that are structured.

Answer 2

False

Question 3

The main purpose of establishing the corpus is to collect all of the documents related to the context being studied.

Answer 3

True

Question 4

Stemming is the process of reducing inflected words to their base or root form.

Question 5

By applying a learning algorithm to parsed text, researchers from Stanford University's NLP lab have
developed methods that can automatically identify the concepts and relationships between those concepts in the text.

Question 6

DARPA and MITRE teamed up to develop capabilities to automatically filter text-based information sources to generate actionable information in a timely manner.

Question 7

Amazon.com leverages Web usage history dynamically and recognizes the user by reading a cookie written by a Web site on the visitor's computer.

Question 8

Customer experience management applications gather and report direct feedback from site visitors by benchmarking against other sites and offline channels, and by supporting predictive modeling of future visitor behavior.

Question 9

Text mining is important to competitive advantage because knowledge is power, and knowledge is derived from text data sources.

Question 10

Web crawlers are Web content mining tools that are used to read through the content of a Web site automatically.

Question 11

The purpose and processes of text mining are different from those of data mining because with text mining the input to the process are data files such as Word documents, PDF files, text excerpts, and XML files.

Question 12

The main categories of knowledge extraction methods are recall, search, and signaling.

Question 13

The goal of natural language processing (NLP) is syntax-driven text manipulation.

Question 14

The quality of search results is impossible to measure accurately using strictly quantitative measures such as click-through rate, abandonment, and search frequency. Additional quantitative and qualitative measures are required.

Question 15

The benefits of text mining are greatest in areas where very large amounts of textual data are being generated, such as law, academic research, finance, and medicine.

Question 16

Unstructured data has a predetermined format. It is usually organized into records as categorical, ordinal, and continuous variables and stored in databases.

Question 17

Web pages consisting of unstructured textual data coded in HTML and logs of visitors' interactions provide rich data that can easily provide effective and efficient knowledge discovery.

Question 18

Text mining can be used to increase cross-selling and up-selling by analyzing the unstructured data generated by call centers.

Question 19

Stop words, such as a, am, the, and was, are words that are filtered out prior to or after processing of natural language data.

Question 20

Compared to polygraphs for deception-detection, text-based deception detection has the advantages of being nonintrusive and widely applicable to textual data and transcriptions of voice recordings.

Question 21

In ________, the problem is to group an unlabelled collection of objects, such as documents, customer comments, and Web pages into meaningful groups without any prior knowledge.

A) search recall
B) classification
C) clustering
D) grouping

Question 22

Which of the following correctly defines a text mining term?

A) Tagging is the number of times a word is found in a specific document.
B) A token is an uncategorized block of text in a sentence.
C) Rooting is the process of reducing inflected words to their base form.
D) A term is a single word or multiword phrase extracted directly from the corpus by means of NLP methods.

Question 23

Commercial software tools include all of the following except:

A) GATE
B) IBM Intelligent Miner Data Mining Suite
C) SAS Text Miner
D) SPSS Text Mining

Question 24

Forward-thinking companies like Ask.com, Scholastic, and St. John Health System are actively using Web mining systems to answer important questions of "Who?" "Why?" and "How?" The benefits of integrating these systems:

A) are measured qualitatively in terms of customer satisfaction, but not measured using financial or other quantitative measure.
B) can be significant in terms of incremental financial growth and increasing customer loyalty and satisfaction.
C) have not yet outweighed the costs of the Web mining systems and analysis.
D) can be infinitely measurable.

Question 25

________ is a branch of the field of linguistics and a part of natural language processing that studies the internal structure of words.

A) Morphology
B) Corpus
C) Stemming
D) Polysemes

Question 26

A ________ is one or more Web pages that provide a collection of links to authoritative pages, reference sites, or a resource list on a specific topic.

A) hub
B) hyperlink-induced topic search
C) spoke
D) community

Question 27

A simple keyword-based search engine suffers from several deficiencies, which include all of the following except:

A) a topic of any breath can easily contain hundreds or thousands of documents
B) many documents that are highly relevant to a topic may not contain the exact keywords defining them
C) web mining can identify authoritative Web pages
D) many of the search results are marginally or not relevant to the topic

Question 28

Which of the following refers to developing useful information from the links included in the Web documents?

A) Web content mining
B) Web subject mining
C) Web structure mining
D) Web matter mining

Question 29

Using ________ as a rich source of knowledge and a strategic weapon, Kodak not only survives but excels in its market segment defined by innovation and constant change.

A) visualization
B) deception detection
C) patent analysis
D) semantic cues

Question 30

It has been shown that the bag-of-word method may not produce good enough information content for text mining tasks. More advanced techniques such as ________ are needed.

A) classification
B) natural language processing
C) evidence-based processing
D) symbolic processing

Question 31

At a very high level, the text mining process consists of each of the following tasks except:

A) create log frequencies
B) establish the corpus
C) create the term-document matrix
D) extract the knowledge

Question 32

Why will computers probably not be able to understand natural language the same way and with the same accuracy that humans do?

A) A true understanding of meaning requires extensive knowledge of a topic beyond what is in the words, sentences, and paragraphs.
B) The natural human language is too specific.
C) The part of speech depends only on the definition and not on the context within which it is used.
D) All of the above.

Question 33

Which of the following is not one of the three main areas of Web mining?

A) Web search mining
B) Web content mining
C) Web structure mining
D) Web usage mining

Question 34

When registered users revisit Amazon.com, they are greeted by name. This task involves recognizing the user by ________.

A) pattern discovery
B) association
C) text mining
D) reading a cookie

Question 35

Text mining is the semi-automated process of extracting ________ from large amounts of unstructured data sources.

A) patterns
B) useful information
C) knowledge
D) all of the above

Question 36

The two main approaches to text classification are ________ and ________.

A) knowledge engineering; machine learning
B) categorization; clustering
C) association; trend analysis
D) knowledge extraction; association

Question 37

Why does the Web pose great challenges for effective and efficient knowledge discovery?

A) The Web search engines are indexed-based.
B) The Web is too dynamic.
C) The Web is too specific to a domain.
D) The Web infrastructure contains hyperlink information.

Question 38

A vast majority of business data are stored in text documents that are ________.

A) mostly quantitative
B) virtually unstructured
C) semi-structured
D) highly structured

Question 39

All of the following are popular application areas of text mining except:

A) information extraction
B) document summarization
C) question answering
D) data structuring

Question 40

All of the following are types of data generated through Web page visits except:

A) data stored in server access logs, referrer logs, agent logs, and client-side cookies
B) user profiles
C) hyperlink analysis
D) metadata, such as page attributes, content attributes, and usage data

Question 41

________ is an important component of text mining and is a subfield of artificial intelligence and computational linguistics. It studies the problem of understanding the natural human language.

Question 42

________ mining is the extraction of useful information from data generated through Web page visits and transactions.

Question 43

________ mining is the process of extracting useful information from the links embedded in Web documents.

Question 44

________ is the process of reducing inflected words to their base or root form.

Question 45

One of the main approaches to text classification is ________ in which an expert's knowledge is encoded into the system either declaratively or in the form of procedural classification rules.

Question 46

A(n) ________ is one or more Web pages that provide a collection of links to authoritative pages.

Question 47

Fundamental to the optimization process is ________, gathering data and information that can then be transformed into tangible analysis and recommendations for improvement using Web mining tools and techniques.

Question 48

The term "stop-words" are used by text mining to ________ commonly used words.

Question 49

At a very high level, the first of three consecutive tasks in the text mining process is to establish the ________, which is a list of organized documents.

Question 50

________ is the semi-automated process of extracting patterns from large amounts of unstructured data sources.

Question 51

In linguistics, a(n) ________ is a large and structured set of texts prepared for the purpose of conducting knowledge discovery.

Question 52

In the text mining process, the output of task two is a flat file called a ________ matrix where the cells are populated with the term frequencies.

Question 53

________ words or noise words are words that are filtered out prior to or after processing of natural language data.

Question 54

Web analytics, CEM, and VOC applications form the foundation of the Web site ________ ecosystem that supports the online business' ability to positively influence desired outcomes.

Question 55

The ________ model, which is one where multiple sources of data describing the same population are integrated to increase the depth and richness of the resulting analysis, forms the framework of the Web site optimization ecosystem.

Question 56

________ applications focus on "who and how" questions by gathering and reporting direct feedback from site visitors, by benchmarking against other sites and offline channels, and by supporting predictive modeling of future visitor behavior.

Question 57

Analysis of the information collected by Web servers can help better understand user behavior. Analysis of this data is called ________ analysis.

Question 58

________ is the grouping of similar documents without having a predefined set of categories.

Question 59

________ analysis is a technique used to detect favorable and unfavorable opinions toward specific products and services using textual data sources, such as customer feedback in Web postings and the detection of unfavorable rumors.

Question 60

________ is the process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases, where the data are organized in records structured by categorical, ordinal, or continuous variables.

Question 61

List three business applications of Web mining.

Question 62

List two options for managing or reducing the dimensionality (size) of the term-document matrix (TDM).

Question 63

What is the primary purpose of text mining within the context of knowledge discovery?

Question 64

Compare and contrast text mining and data mining.

Question 65

Diagram and explain the three-step text mining process.

Question 66

NLP has successfully been applied to a variety of tasks via computer programs to automatically process natural human language that previously could only be done by humans. List three of the most popular of these tasks.

Question 67

Define the three main areas of Web mining and each area's source of information.

Question 68

Why will computers probably not be able to understand natural language the same way and with the same accuracy that humans do?

Question 69

Describe a marketing application of text mining.

Question 70

What are three of the challenges for effective and efficient knowledge discovery posed by the Web?